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Are uncertainty shocks a major source of business cycle fluctuations? This paper studies the effect of a mean preserving shock to the variance of aggregate total factor productivity (macro uncertainty) and to the dispersion of entrepreneurs' idiosyncratic productivity (micro uncertainty) in a financial accelerator DSGE model with sticky prices. It explores the different mechanisms through which uncertainty shocks are propagated and amplified. The time series properties of macro and micro uncertainty are estimated using U.S. aggregate and firm-level data, respectively. While surprise increases in micro uncertainty have a larger impact on output than macro uncertainty, these account for a small (non-trivial) share of output volatility.
The Fall 2017 IMF Research Bulletin includes a Q&A article covering "Seven Questions on the Globalization of Farmland" by Christian Bogmans. The first research summary, by Manmohan Singh and Haobing Wang is "Central Bank Balance Sheet Policies: Some Policy Implications." The second research summary is "Leaning Against the Windy Bank Lending" by Giovanni Melina and Stefania Villa. A listing of new IMF Working Papers and Staff Discussion Notes is featured, as well as new titles from IMF Publications. Information on IMF Economic Review is also included.
The paper explores the nexus between the financial and business cycles in a semi-structural New Keynesian model with a financial accelerator, an active banking sector, and an endogenous macroprudential policy reaction function. We parametrize the model for Luxembourg through a mix of calibration and Bayesian estimation techniques. The model features dynamic properties that align with theoretical priors and empirical evidence and displays sensible data-matching and forecasting capabilities, especially for credit indicators. We find that the credit gap, which remained positive during COVID-19 amid continued favorable financial conditions and policy support, had been closing by mid-2022. Model-based forecasts using data up to 2022Q2 and conditional on the October 2022 WEO projections for the Euro area suggest that Luxembourg's business and credit cycles would deteriorate until late 2024. Based on these insights about the current and projected positions in the credit cycle, the model can guide policymakers on how to adjust the macroprudential policy stance. Policy simulations suggest that the weights given to measures of credit-to-GDP and asset price gaps in the macroprudential policy rule should be well-calibrated to avoid unwarranted volatility in the policy response.
Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data.The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.
Watch this video interview with Greg Mankiw and Larry Ball discussing the future of the intermediate macroeconomics course and their new text. Check out preview content for Macroeconomics and the Financial System here. The financial crisis and subsequent economic downturn of 2008 and 2009 was a dramatic reminder of what economists have long understood: developments in the overall economy and developments in the financial system are inextricably intertwined. Derived and updated from two widely acclaimed textbooks (Greg Mankiw’s Macroeconomics, Seventh Edition and Larry Ball’s Money, Banking, and the Financial System), this groundbreaking text is the first and only intermediate macroeconomics text that provides substantial coverage of the financial system.
This introduction to modern business cycle theory uses a neoclassical growth framework to study the economic fluctuations associated with the business cycle. Presenting advances in dynamic economic theory and computational methods, it applies concepts to t
Traditionally, economic growth and business cycles have been treated independently. However, the dependence of GDP levels on its history of shocks, what economists refer to as “hysteresis,” argues for unifying the analysis of growth and cycles. In this paper, we review the recent empirical and theoretical literature that motivate this paradigm shift. The renewed interest in hysteresis has been sparked by the persistence of the Global Financial Crisis and fears of a slow recovery from the Covid-19 crisis. The findings of the recent literature have far-reaching conceptual and policy implications. In recessions, monetary and fiscal policies need to be more active to avoid the permanent scars of a downturn. And in good times, running a high-pressure economy could have permanent positive effects.
Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Yes, it makes a lot of sense. This paper studies how to design simple loss functions for central banks, as parsimonious approximations to social welfare. We show, both analytically and quantitatively, that simple loss functions should feature a high weight on measures of economic activity, sometimes even larger than the weight on inflation. Two main factors drive our result. First, stabilizing economic activity also stabilizes other welfare relevant variables. Second, the estimated model features mitigated inflation distortions due to a low elasticity of substitution between monopolistic goods and a low interest rate sensitivity of demand. The result holds up in the presence of measurement errors, with large shocks that generate a trade-off between stabilizing inflation and resource utilization, and also when ensuring a low probability of hitting the zero lower bound on interest rates.
Policymakers often face difficult tradeoffs in pursuing domestic and external stabilization objectives. The paper reflects staff’s work to advance the understanding of the policy options and tradeoffs available to policymakers in a systematic and analytical way. The paper recognizes that the optimal path of the IPF tools depends on structural characteristics and fiscal policies. The operational implications of IPF findings require careful consideration. Developing safeguards to minimize the risk of inappropriate use of IPF policies will be essential. Staff remains guided by the Fund’s Institutional View (IV) on the Liberalization and Management of Capital Flows.